Secondary use of routinely collected administrative health data for epidemiologic research: Answering research questions using data collected for a different purpose
Bibliographic record
Abstract
The use of routinely collected administrative health data for research can provide unique insights to inform decision-making and, ultimately, support better public health outcomes. Yet, since these data are primarily collected to administer healthcare service delivery, challenges exist when using such data for secondary purposes, namely epidemiologic research. Many of these challenges stem from the researcher's lack of control over the quality and consistency of data collection, and - furthermore - a lessened understanding of the data being analyzed. That said, we assert that these challenges can be partly mitigated through careful, systematic use of these data in epidemiologic research. This article presents considerations derived from experiences analyzing administrative health data (e.g., healthcare practitioner billings, hospitalizations, and prescription medication data) in the Canadian province of British Columbia (population of over 5 million in 2024), though we believe the underlying principles generalize beyond this region. Key considerations were organized around four themes: 1) Know the data and their primary use (understand their scope and limitations); 2) Understand classification and coding systems (appreciate the nuances regarding classification systems, versions, how they are employed in the primary uses of the data, and querying the values); 3) Transform data into meaningful forms (process data and apply identification algorithms, when necessary); 4) Recognize the importance of validity when defining analytic variables (make meaningful inferences based on data/algorithms). Although this article is not an exhaustive list of all considerations, we believe that it will provide pragmatic insights for those interested in leveraging administrative health data for epidemiologic research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.336 | 0.621 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.023 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".